Method and system for tagging an individual in a digital image

ABSTRACT

A system and method for tagging an image of an individual in a plurality of photos is disclosed herein. A feature vector of an individual is used to analyze a set of photos on a social networking website such as www.facebook.com to determine if an image of the individual is present in a photo of the set of photos. Photos having an image of the individual are tagged preferably by listing a URL or URI for each of the photos in a database.

CROSS REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/867,023, filed on Jan. 10, 2018; which is a continuation of U.S.patent application Ser. No. 14/094,752, filed on Dec. 2, 2013, now U.S.Pat. No. 9,875,395, issued Jan. 23, 2018; which is a continuation ofU.S. patent application Ser. No. 13/753,543, filed on Jan. 30, 2013, nowU.S. Pat. No. 8,798,321 issued Aug. 5, 2014; which is a continuation ofU.S. patent application Ser. No. 12/341,318, filed on Dec. 22, 2008, nowU.S. Pat. No. 8,369,570, issued on Feb. 5, 2013; which claims priorityto U.S. Provisional Patent No. 61/016,800, filed on Dec. 26, 2007; andis a continuation-in-part of U.S. patent application Ser. No.11/534,667, filed on Sep. 24, 2006, now U.S. Pat. No. 7,450,740, issuedon Nov. 11, 2008; which claims priority to U.S. Provisional PatentApplication No. 60/721,226, filed Sep. 28, 2005; the contents of each ofwhich are incorporated herein by reference in their entirety.

BACKGROUND OF THE INVENTION Field of the Invention

The present invention relates to a method and system for meta-tagging acollection of digital photos containing an image of an individual orindividuals.

Description of the Related Art

Classification of facial images using feature recognition software iscurrently used by various government agencies such as the Department ofHomeland Security (DHS) and the Department of Motor Vehicles (DMV) fordetecting terrorists, detecting suspected cases of identity fraud,automating border and passport control, and correcting mistakes in theirrespective facial image databases. Facial images stored in the DMV orDHS are digitized and stored in centralized databases, along withassociated information on the person. Examples of companies that providebiometric facial recognition software include Cross Match Technologies,Cognitec, Cogent Systems, and Iridian Technologies; of these, Cognitecalso provides a kiosk for digitally capturing images of people forstorage into their software.

Your face is an important part of who you are and how people identifyyou. Imagine how hard it would be to recognize an individual if allfaces looked the same. Except in the case of identical twins, the faceis arguably a person's most unique physical characteristic. While humanshave had the innate ability to recognize and distinguish different facesfor millions of years, computers are just now catching up.

Visionics, a company based in New Jersey, is one of many developers offacial recognition technology. The twist to its particular software,FACEIT, is that it can pick someone's face out of a crowd, extract thatface from the rest of the scene and compare it to a database full ofstored images. In order for this software to work, it has to know what abasic face looks like. Facial recognition software is based on theability to first recognize faces, which is a technological feat initself, and then measure the various features of each face.

If you look in the mirror, you can see that your face has certaindistinguishable landmarks. These are the peaks and valleys that make upthe different facial features. Visionics defines these landmarks asnodal points. There are about 80 nodal points on a human face. A few ofthe nodal points that are measured by the FACEIT software: distancebetween eyes; width of nose; depth of eye sockets; cheekbones; Jaw line;and chin. These nodal points are measured to create a numerical codethat represents the face in a database. This code is referred to as afaceprint and only fourteen to twenty-two nodal points are necessary forthe FACEIT software to complete the recognition process.

Facial recognition methods may vary, but they generally involve a seriesof steps that serve to capture, analyze and compare your face to adatabase of stored images. The basic process that is used by the FACEITsoftware to capture and compare images is set forth below and involvesDetection, Alignment, Normalization, Representation, and Matching. Toidentify someone, facial recognition software compares newly capturedimages to databases of stored images to see if that person is in thedatabase.

Detection is when the system is attached to a video surveillance system,the recognition software searches the field of view of a video camerafor faces. If there is a face in the view, it is detected within afraction of a second. A multi-scale algorithm is used to search forfaces in low resolution. The system switches to a high-resolution searchonly after a head-like shape is detected.

Alignment is when a face is detected, the system determines the head'sposition, size and pose. A face needs to be turned at least thirty-fivedegrees toward the camera for the system to register the face.

Normalization is when the image of the head is scaled and rotated sothat the head can be registered and mapped into an appropriate size andpose. Normalization is performed regardless of the head's location anddistance from the camera. Light does not impact the normalizationprocess.

Representation is when the system translates the facial data into aunique code. This coding process allows for easier comparison of thenewly acquired facial data to stored facial data.

Matching is when the newly acquired facial data is compared to thestored data and linked to at least one stored facial representation.

The heart of the FACEIT facial recognition system is the Local FeatureAnalysis (LFA) algorithm. This is the mathematical technique the systemuses to encode faces. The system maps the face and creates thefaceprint. Once the system has stored a faceprint, it can compare it tothe thousands or millions of faceprints stored in a database. Eachfaceprint is stored as an 84-byte file.

One of the first patents related to facial recognition technology isRothfjell, U.S. Pat. No. 3,805,238 for a Method for IdentifyingIndividuals using Selected Characteristics Body Curves. Rothfjellteaches an identification system in which major features (e.g. the shapeof a person's nose in profile) are extracted from an image and stored.The stored features are subsequently retrieved and overlaid on a currentimage of the person to verify identity.

Another early facial recognition patent is Himmel, U.S. Pat. No.4,020,463 for an Apparatus and A Method for Storage and Retrieval ofImage Patterns. Himmel discloses digitizing a scanned image into binarydata which is then compressed and then a sequence of coordinates andvector values are generated which describe the skeletonized image. Thecoordinates and vector values allow for compact storage of the image andfacilitate regeneration of the image.

Yet another is Gotanda, U.S. Pat. No. 4,712,103 for a Door Lock ControlSystem. Gotanda teaches, inter alia, storing a digitized facial image ina non-volatile ROM on a key, and retrieving that image for comparisonwith a current image of the person at the time he/she request access toa secured area. Gotanda describes the use of image compression, by asmuch as a factor of four, to reduce the amount of data storage capacityneeded by the ROM that is located on the key.

Yet another is Lu, U.S. Pat. No. 4,858,000. Lu teaches an imagerecognition system and method for identifying ones of a predeterminedset of individuals, each of whom has a digital representation of his orher face stored in a defined memory space.

Yet another is Tal, U.S. Pat. No. 4,975,969. Tal teaches an imagerecognition system and method in which ratios of facial parameters(which Tal defines a distances between definable points on facialfeatures such as a nose, mouth, eyebrow etc.) are measured from a facialimage and are used to characterize the individual. Tal, like Lu in U.S.Pat. No. 4,858,000, uses a binary image to find facial features.

Yet another is Lu, U.S. Pat. No. 5,031,228. Lu teaches an imagerecognition system and method for identifying ones of a predeterminedset of individuals, each of whom has a digital representation of his orher face stored in a defined memory space. Face identification data foreach of the predetermined individuals are also stored in a UniversalFace Model block that includes all the individual pattern images or facesignatures stored within the individual face library.

Still another is Burt, U.S. Pat. No. 5,053,603. Burt teaches an imagerecognition system using differences in facial features to distinguishone individual from another. Burt's system uniquely identifiesindividuals whose facial images and selected facial feature images havebeen learned by the system. Burt's system also “generically recognizes”humans and thus distinguishes between unknown humans and non-humanobjects by using a generic body shape template.

Still another is Turk et al., U.S. Pat. No. 5,164,992. Turk teaches theuse of an Eigenface methodology for recognizing and identifying membersof a television viewing audience. The Turk system is designed to observea group of people and identify each of the persons in the group toenable demographics to be incorporated in television ratingsdeterminations.

Still another is Deban et al., U.S. Pat. No. 5,386,103. Deban teachesthe use of an Eigenface methodology for encoding a reference face andstoring said reference face on a card or the like, then retrieving saidreference face and reconstructing it or automatically verifying it bycomparing it to a second face acquired at the point of verification.Deban teaches the use of this system in providing security for AutomaticTeller Machine (ATM) transactions, check cashing, credit card securityand secure facility access.

Yet another is Lu et al., U.S. Pat. No. 5,432,864. Lu teaches the use ofan Eigenface methodology for encoding a human facial image and storingit on an “escort memory” for later retrieval or automatic verification.Lu teaches a method and apparatus for employing human facial imageverification for financial transactions.

Technologies provided by wireless carriers and cellular phonemanufacturers enable the transmission of facial or object images betweenphones using Multimedia Messaging Services (MMS) as well as to theInternet over Email (Simple Mail Transfer Protocol, SMTP) and WirelessAccess Protocol (WAP). Examples of digital wireless devices capable ofcapturing and receiving images and text are camera phones provided byNokia, Motorola, LG, Ericsson, and others. Such phones are capable ofhandling images as JPEGs over MMS, Email, and WAP across many of thewireless carriers: Cingular, T-Mobile, (GSM/GPRS), and Verizon (CDMA)and others.

Neven, U.S. Patent Publication 2005/0185060, for an Image Base InquirySystem for Search Engines for Mobile Telephones with Integrated Camera,discloses a system using a mobile telephone digital camera to send animage to a server that converts the image into symbolic information,such as plain text, and furnishes the user links associated with theimage which are provided by search engines.

Neven, et al., U.S. Patent Publication 2006/0012677, for an Image-BasedSearch Engine for Mobile Phones with Camera, discloses a system thattransmits an image of an object to a remote server which generates threeconfidence values and then only generates a recognition output from thethree confidence values, with nothing more.

Adam et al., U.S. Patent Publication 2006/0050933, for a Single ImageBased Multi-Biometric System And Method which integrates face, skin andiris recognition to provide a biometric system.

The general public has a fascination with celebrities and many membersof the general public use celebrities as a standard for judging someaspect of their life. Many psychiatrists and psychologists believe theconfluence of forces coming together in technology and media have led tothis celebrity worship factor in our society. One output of thiscelebrity factor has been a universal approach to compare or determinethat someone looks like a certain celebrity. People are constantlystating that someone they meet or know looks like a celebrity, whetherit is true or not. What would be helpful would be to scientificallyprovide a basis for someone to lay claim as looking like a certaincelebrity.

BRIEF SUMMARY OF THE INVENTION

The present invention provides a novel method and system for taggingdigital photos containing an image of an individual or individuals. Thesystem and method can be used to organize a collection of digital photosposted on a social networking web site.

A feature vector is created for an individual from one or more digitalphotos. This feature vector is then utilized to search a database ofdigital photos and tag those digital photos containing an image of theindividual as determined by a match to the feature vector. The databaseof digital photos may be stored on a social networking web site. Themeta-tagging of a digital photo comprises marking the location (X-Ycoordinate in the digital photo, size in the digital photo, and tilt inthe digital photo) and storing this facial location information alongwith a unique identification reference for the individual, and a uniqueidentification reference for the digital photo (this can be a URL or URIfor the digital photo if on a web site). The actual digital photo is notmodified in any manner.

The digital image is preferably captured by a wireless communicationdevice (preferably a mobile telephone) or from a personal computer (PC).The image is preferably in a JPEG, TIFF, GIF or other standard imageformat. Further, an analog image may be utilized if digitized. The imageis sent to the wireless carrier and subsequently sent over the internetto an image classification server. Alternatively, the digital image maybe uploaded to a PC from a digital camera or scanner and then sent tothe image classification server over the internet.

After an image is received by the image classification server, the imageis processed into a feature vector, which reduces the complexity of thedigital image data into a small set of variables that represent thefeatures of the image that are of interest for classification purposes.

The feature vector is compared against existing feature vectors in animage database to find the closest match. The image database preferablycontains one or more feature vectors for each target individual.

The digital photo used for the creating a feature vector may be createdin a number of different methods. The user may capture a digital imagewith a digital camera enabled wireless communication device, such as amobile telephone. The compressed digital image is sent to the wirelesscarrier as a multimedia message (MMS), a short message service (“SMS”),an e-mail (Simple Mail Transfer Protocol (“SMTP”)), or wirelessapplication protocol (“WAP”) upload. The image is subsequently sent overthe internet using HTTP or e-mail to an image classification server.Alternatively, the digital image(s) may be uploaded to a PC from adigital camera, or scanner. Once on the PC, the image(s) can betransferred over the internet to the image classification server as ane-mail attachment, or HTTP upload.

After the image is received by the image classification server, afeature vector is generated for the image. A feature vector is a smallset of variables that represent the features of the image that are ofinterest for classification purposes. Creation and comparison offeatures vectors may be queued, and scaled across multiple machines.Alternatively, different feature vectors may be generated for the sameimage. Alternatively, the feature vectors of several images of the sameindividual may be combined into a single feature vector. The incomingimage, as well as associate features vectors, may be stored for laterprocessing, or added to the image database. For faces, possible featurevector variables are the distance between the eyes, the distance betweenthe center of the eyes, to the chin, the size, and shape of theeyebrows, the hair color, eye color, facial hair if any, and the like.

One aspect of the present invention is a method for tagging an image ofan individual in a plurality of photos. The method includes providing afirst plurality of photos. Each of the first plurality of photoscomprises an identified image of the individual. The method alsoincludes processing the image of the individual in each of the firstplurality of photos to generate a feature vector for the individual. Themethod also includes analyzing a second plurality of photos to determineif an image of the individual is present in a photo of the secondplurality of photos. The analysis comprises determining if an image ineach of the photos of the second plurality of photos matches the featurevector for the individual. The method also includes identifying each ofthe photos of the second plurality of photos having an image of theindividual to create a third plurality of photos. The method alsoincludes tagging each of the photos of the third plurality of photos toidentify the image of the individual in each of the third plurality ofphotos.

Another aspect of the present invention is a system for tagging an imageof an individual in a plurality of photos. The system includes anetwork, a database, a server engine, a second plurality of photos on asocial networking web site, analysis means, identification means andtagging means. The database comprises a first plurality of photos of animage of an individual. The server engine processes the first pluralityof photos to generate a feature vector for the image of the individual.The analysis means analyzes the second plurality of photos to determineif an image of the individual is present in a photo of the secondplurality of photos. The analysis comprises determining if an image ineach of the photos of the second plurality of photos matches the featurevector for the individual. The identification means identifies each ofthe photos of the second plurality of photos having an image of theindividual to create a third plurality of photos. The tagging means tagseach of the photos of the third plurality of photos to identify theimage of the individual in each of the third plurality of photos.

Yet another aspect of the present invention is a method for meta-taggingan image of an individual in a plurality of photos. The method includesproviding a first plurality of photos. Each of the first plurality ofphotos comprises an identified image of the individual. The method alsoincludes processing the image of the individual in each of the firstplurality of photos to generate a feature vector for the individual. Themethod also includes analyzing a second plurality of photos to determineif an image of the individual is present in a photo of the secondplurality of photos. The analysis comprises determining if an image ineach of the photos of the second plurality of photos matches the featurevector for the individual. The method also includes identifying each ofthe photos of the second plurality of photos having an image of theindividual to create a third plurality of photos. The method alsoincludes meta-tagging each of the photos of the third plurality ofphotos to identify the image of the individual in each of the thirdplurality of photos.

Yet another aspect of the present invention is a method for tagging animage of an individual in a plurality of photos. The method includescreating a feature vector for an individual from a plurality ofreference photos. The method also includes analyzing a second pluralityof photos to determine if an image of the individual is present in aphoto of the second plurality of photos. The analysis comprisesdetermining if an image in each of the photos of the second plurality ofphotos matches the feature vector for the individual. The method alsoincludes identifying each of the photos of the second plurality ofphotos having an image of the individual to create a third plurality ofphotos. The method also includes determining the location the locationof the image in the photo. The method also includes storing the locationof the image in the photo in a database. The method also includesstoring an identifier for the photo in a database. The method alsoincludes storing an identifier for the individual in a database.

Having briefly described the present invention, the above and furtherobjects, features and advantages thereof will be recognized by thoseskilled in the pertinent art from the following detailed description ofthe invention when taken in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1 is a schematic diagram of a system of the present invention.

FIG. 2 is a digital photo with a facial image of an individual.

FIG. 3 is the facial image of FIG. 2 with feature vector indicators.

FIG. 4 is an illustration of digital photos from a social networkingwebsite.

FIG. 4A is an illustration of one of the digital photos from FIG. 4 withlocation information illustrated.

FIG. 5 is a flow chart of a specific method of the present invention.

FIG. 6 is a flow chart of a specific method of the present invention.

DETAILED DESCRIPTION OF THE INVENTION

A system of the present invention is generally illustrated in FIG. 1. Auser 55 can use a mobile telephone to transmit an image or images over anetwork to a processing server 65 for processing. Alternatively, theuser 55 can use a computer to transmit the images to the processingserver 65 over the internet. As discussed in more detail below, theprocessing server 65 creates a feature vector from the image or images.The processing server can then access a database of digital photos 75 todetermine if any of the digital photos can the image. If the databasephotos contain the image, information is generated for each image andstored on a database 70.

Generally, a facial image is transmitted over a network to an imageclassification server or processing server, preferably over a wirelessnetwork. The facial image is preferably sent over the internet usingHTTP or e-mail to the image classification server. The facial image,preferably a compressed digital facial image such as a JPEG image, issent to a wireless carrier as a MMS, a SMS, a SMTP, or WAP upload.Alternatively, the facial image is uploaded to a computer from a digitalcamera, or scanner and then transferred over the internet to the imageclassification server as an e-mail attachment, or HTTP upload.

The facial image is analyzed at the image classifications server todetermine if the facial image is of adequate quality to be processed formatching. Quality issues with the facial image include but are notlimited to a poor pose angle, brightness, shading, eyes closed,sunglasses worn, obscured facial features, or the like. Processing ofthe image preferably comprises using an algorithm which includes aprinciple component analysis technique to process the face of the facialimage into an average of a multitude of faces, otherwise known as theprinciple component and a set of images that are the variance from theaverage face image known as the additional components. Each isreconstructed by multiplying the principal components and the additionalcomponents against a feature vector and adding the resulting imagestogether. The resulting image reconstructs the original face of thefacial image. Processing of the facial image comprises factors such asfacial hair, hair style, facial expression, the presence of accessoriessuch as sunglasses, hair color, eye color, and the like. Essentially aprimary feature vector is created for the facial image. This primaryfeature vector is compared to a plurality of database of imagespreferably located on a social networking website. A more detaileddescription of generating feature vectors is disclosed in Shah, et al.,U.S. Pat. No. 7,450,740, for an Image Classification and InformationRetrieval Over Wireless Digital Networks and The Internet, which ishereby incorporated by reference in its entirety.

The present invention preferably uses facial recognition softwarecommercially or publicly available such as the FACEIT brand softwarefrom IDENTIX, the FACEYACS brand software from COGNETIC, and others.Those skilled in the pertinent art will recognize that there are manyfacial recognition softwares, including those in the public domain, thatmay be used without departing from the scope and spirit of the presentinvention.

The operational components of the image classification server/processingserver 65 preferably include an input module, transmission engine, inputfeed, feature vector database, sent images database, facial recognitionsoftware, perception engine, and output module. The input module isfurther partitioned into wireless device inputs, e-mail inputs and HTTP(internet) inputs.

A digital photo 100 of a facial image of an individual is shown in FIG.2. The digital photo is sent to the processing server for creation of afeature vector for this individual. The feature vector is generatedbased on facial features, and this allows the image of the individual tobe distinguished within other digital photos. Such features include thehair color 102, face shape 104, distance between eyes 106, hair style108, distance between eyes and mouth 110, length of mouth 112 and noseshape 114, and other like features. The primary feature vector is thenused to identify other digital photos bearing an image of theindividual. As shown in FIG. 4, a collection of digital photos bearingan image of the individual are identified. In FIG. 4A, a particularphoto bearing an image of the individual is analyzed for locationinformation which is preferably stored in a database 70. An X-Y positionof the image is determined, along with the size of the image and tiltangle. This allows image to be quickly identified.

A method 400 for tagging an image of an individual in a plurality ofphotos is shown in FIG. 5. In this method, at block 402, a first set ofdigital photos is provided with each of the digital photos containing animage of an individual. The first set of photos is preferably providedto a processing server over a network. At block 404, the image or imagesof the individual is/are processed, preferably at the processing server,to generate a feature vector for the image(s) of the individual. Atblock 406, a second set of photos is analyzed, preferably by the server,to determine if any of the photos of the second set of photos has animage that matches the feature vector. The second set of photos ispreferably located on a social networking website. At block 408, photosof the second set of photos that contain an image that matches thefeature vector are identified, preferably by the processing server. Atblock 410, these identified photos are tagged to create a third set ofphotos.

A method 500 for tagging a facial image of an individual in a pluralityof digital photos, is shown in FIG. 6. For example, a user may want tocreate links to unorganized digital photos bearing an image of anindividual or group of individuals. The present method allows the userto create such links. At block 502, a feature vector for a facial imageof an individual is created at a processing server. The feature vectoris preferably created from a first set of photos containing the facialimage of the individual. At block 504, a second set of digital photos isanalyzed, preferably by the processing server, to determine if any ofthe digital photos of the second set of photos has a facial image thatmatches the feature vector. The second set of photos is preferablylocated on a social networking website. At block 506, photos of thesecond set of photos that contain an image that matches the featurevector are identified, preferably by the processing server. At block508, the location information of the facial image in each of the secondset of digital photos is determined by the processing server. Thelocation information is preferably the X and Y coordinates, the size ofthe facial image and the tilt angle of the facial image in the digitalphoto. At block 510, an identifier and the location information of thefacial image for each of the identified digital photos is stored on adatabase, preferably at the processing server.

From the foregoing it is believed that those skilled in the pertinentart will recognize the meritorious advancement of this invention andwill readily understand that while the present invention has beendescribed in association with a preferred embodiment thereof, and otherembodiments illustrated in the accompanying drawings, numerous changesmodification and substitutions of equivalents may be made thereinwithout departing from the spirit and scope of this invention which isintended to be unlimited by the foregoing except as may appear in thefollowing appended claim. Therefore, the embodiments of the invention inwhich an exclusive property or privilege is claimed are defined in thefollowing appended claims.

1. (canceled)
 2. A system comprising: a server to support a web site;and a data store accessible by the server and configured to storeimage-specific information, including classification and locationinformation, obtained from a plurality of processed facial images ofidentified individuals, and the server configured to: generate theimage-specific information including a facial tilt angle; receive asubject photo containing an unknown facial image of an individual;generate a set of variables from the unknown facial image; determine apredicted identity of the unknown facial image based on an analysis ofthe set of variables relative to at least some of the image-specificinformation; and transmit the predicted identity over a network to acomputing device.
 3. The system of claim 2 wherein the web site is asocial networking web site.
 4. The system of claim 2 wherein thecomputing device is a wireless computing device.
 5. The system of claim2 wherein the subject photo includes a URL or URI.
 6. A methodcomprising: generating and storing image-specific information, includingclassification and location information, obtained from a plurality ofprocessed facial images of identified individuals, the image-specificinformation including a facial tilt angle; receiving a subject photocontaining an unknown facial image of an individual; generating a set ofvariables from the unknown facial image; determining a predictedidentity of the unknown facial image based on an analysis of the set ofvariables relative to at least some of the image-specific information;and transmitting the predicted identity over a network to a computingdevice, wherein the method is carried out within a system that includesa server supporting a web site.
 7. The method of claim 6 wherein the website is a social networking web site.
 8. The method of claim 6 whereinthe computing device is a wireless computing device.
 9. The method ofclaim 6 wherein the subject photo includes a URL or URI.
 10. The methodof claim 6 wherein the receiving the subject photo comprises receivingthe subject photo as an email attachment.
 11. The method of claim 6wherein the receiving the subject photo comprises receiving the subjectphoto as HTTP upload.